Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace-Reference-Cited by-同舟云学术

Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace

Published:2023-12 Issue: Volume:182 Page:108317
ISSN:0160-4120
Container-title:Environment International
language:en
Short-container-title:Environment International

Author:

Chang Ta-Yuan^ORCID,Chen Guan-Yu,Chen Jing-Jie,Young Li-Hao^ORCID,Chang Li-Te

Funder

Taiwan Ministry of Science and Technology

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

General Environmental Science

Reference39 articles.

1. Ali, M. 2020. PyCaret: An open source, low-code machine learning library in Python. https://www.pycaret.org/ (accessed 11 April 2022).

2. A machine learning approach to objective identification of dust in satellite imagery;Berndt;Earth Space Sci.,2021

3. Respirable quartz dust exposure and airway obstruction: a systematic review and meta-analysis;Bruske;Occup. Environ. Med.,2014

4. Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA;Carvlin;J. Air Waste Manag. Assoc.,2017

5. Application of land-use regression models to estimate sound pressure levels and frequency components of road traffic noise in Taichung;Chang;Taiwan. Environ Int,2019

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